Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/113800
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dc.contributorDepartment of Mechanical Engineeringen_US
dc.creatorShao, Xen_US
dc.creatorZhang, Ben_US
dc.creatorZhi, Hen_US
dc.creatorRomero, JGen_US
dc.creatorFan, Ben_US
dc.creatorHu, Qen_US
dc.creatorNavarroAlarcon, Den_US
dc.date.accessioned2025-06-24T06:38:00Z-
dc.date.available2025-06-24T06:38:00Z-
dc.identifier.issn1083-4435en_US
dc.identifier.urihttp://hdl.handle.net/10397/113800-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.rights© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.rightsThe following publication X. Shao et al., "InPTC: Integrated Planning and Tube-Following Control for Prescribed-Time Collision-Free Navigation of Wheeled Mobile Robots," in IEEE/ASME Transactions on Mechatronics, vol. 30, no. 5, pp. 3339-3350, Oct. 2025 is available at https://doi.org/10.1109/TMECH.2024.3486727.en_US
dc.subjectCollision avoidanceen_US
dc.subjectPath planningen_US
dc.subjectPrescribed-time controlen_US
dc.subjectTrajectory trackingen_US
dc.subjectWheeled mobile robots (WMR)en_US
dc.titleInPTC : integrated planning and tube-following control for prescribed-time collision-free navigation of wheeled mobile robotsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage3339en_US
dc.identifier.epage3350en_US
dc.identifier.volume30en_US
dc.identifier.issue5en_US
dc.identifier.doi10.1109/TMECH.2024.3486727en_US
dcterms.abstractIn this article, we propose a novel approach, called integrated planning and tube-following Control (InPTC), for prescribed-time collision-free navigation of wheeled mobile robots in a compact convex workspace cluttered with static, sufficiently separated, and convex obstacles. A path planner with prescribed-time convergence is presented based upon Bouligand's tangent cones and time scale transformation (TST) techniques, yielding a continuous vector field that can guide the robot from almost all initial positions in the free space to the designated goal at a prescribed time, while avoiding entering the obstacle regions augmented with a safety margin. By leveraging barrier functions and TST, we further derive a tube-following controller to achieve robot trajectory tracking within a prescribed time less than the planner's settling time. This controller ensures the robot moves inside a predefined 'safe tube' around the reference trajectory, where the tube radius is set to be less than the safety margin. Consequently, the robot will reach the goal location within a prescribed time while avoiding collision with any obstacles along the way. The proposed InPTC is implemented on a Mona robot operating in an arena cluttered with obstacles of various shapes. Experimental results demonstrate that InPTC not only generates smooth collision-free reference trajectories that converge to the goal location at the preassigned time of 250s (i.e., the required task completion time), but also achieves tube-following trajectory tracking with tracking accuracy higher than 0.01m after the preassigned time of 150s. This enables the robot to accomplish the navigation task within the required time of 250s.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE/ASME transactions on mechatronics, Oct. 2025, v. 30, no. 5, p. 3339-3350en_US
dcterms.isPartOfIEEE/ASME transactions on mechatronicsen_US
dcterms.issued2025-10-
dc.identifier.scopus2-s2.0-85209387777-
dc.identifier.eissn1941-014Xen_US
dc.description.validate202506 bcchen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera3769b-
dc.identifier.SubFormID51005-
dc.description.fundingSourceRGCen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryGreen (AAM)en_US
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